Senior Engineer
Location: Bengaluru, India
Experience: 5+ years preferred; exceptional engineers with strong research projects or open-source contributions are welcome.
About Areta360
Founded in 2024, ARETA360 is a next-generation AI company building intelligent solutions across domains. We develop cutting-edge AI technologies that power real-time omnichannel experiences and personalized customer interactions driving innovation at scale.
About NORA-Our platform
NORA is building a new intelligence layer for business data and AI systems. We are looking for a senior engineer who wants to create the underlying intelligence technology.
The Role
As a Senior Engineer, you will work at the intersection of AI research, computer science, semantic systems, and data engineering. This is a hands-on, first-principles engineering role.
What You Will Build
1. Semantic Representation & Machine Reasoning
Design machine-readable representations of intent, meaning, relationships, constraints, and business knowledge.
Develop semantic intermediate representations, typed structures, and canonical forms that reduce ambiguity between human intent and machine execution.
Build mechanisms for semantic grounding, entity resolution, relationship discovery, and constraint validation.
Explore knowledge graphs, ontologies, symbolic reasoning, and hybrid neural-symbolic architectures.
Investigate how semantic representations can be reused across analytics, AI agents, and intelligent applications.
2. Core AI Algorithms & Intelligence Infrastructure
Design algorithms and processing pipelines rather than relying exclusively on existing AI frameworks.
Develop structured interpretation, planning, validation, and execution mechanisms.
Combine language models with deterministic algorithms, rule engines, type systems, and constraint solvers.
Research techniques for reducing hallucinations, improving repeatability, and making AI outputs verifiable. Identify opportunities to replace probabilistic generation with deterministic computation where correctness requires it.
3. Research, Experimentation & Benchmarking
Read and implement relevant research papers and technical methods.
Form hypotheses, design-controlled experiments, and establish meaningful baselines.
Develop evaluation frameworks to measure correctness, consistency, robustness, latency, and computational cost.
Analyze failure modes, conduct ablation studies, and improve algorithms based on evidence.
Document findings and translate successful experiments into production-ready components.
4. Data Systems & Production Engineering
Build semantic and intelligence services using Python and PostgreSQL.
Work with parsers, structured data models, query planners, metadata systems, and execution engines.
Design reliable APIs and data pipelines that integrate core intelligence components into NORA.
Write modular, tested, reproducible software suitable for production deployment.
Must-Have Skills
Strong Python programming and computer science fundamentals.
Deep understanding of SQL, relational data models, joins, aggregation, and query execution.
Experience implementing algorithms and designing software systems beyond basic framework integration.
Understanding of NLP, language models, embeddings, and semantic representation.
Strong skills in data structures, algorithms, abstraction, and structured problem-solving.
Experience with systematic testing, benchmarking, and debugging.
Ability to read technical papers, reproduce results, and independently investigate unfamiliar problems.
Strongly Preferred
Compilers, parsers, abstract syntax trees, intermediate representations, or type systems.
Knowledge graphs, ontologies, RDF/OWL, or symbolic reasoning.
Constraint programming, SAT/SMT solvers, or formal methods.
AI model evaluation, fine-tuning, or applied ML research.
Database internals, query optimisation, or execution planning.
Research prototypes, meaningful open-source contributions, or technically substantial independent projects.
Who Should Apply?
You may be a fit if you enjoy questions such as:
How can human intent be represented in a form that machines can reason about reliably?
How can we separate probabilistic interpretation from deterministic execution?
How can an AI system prove that a result follows from the available data and defined rules?
Can a canonical semantic representation make different AI systems more consistent and interoperable?
How can we measure and systematically improve the correctness of intelligent systems?
What You Will Gain
Ownership of foundational AI/ML technology at an early-stage deep-tech company.
The opportunity to move from research ideas to working algorithms and production systems.
Freedom to explore new approaches to semantic intelligence and reliable machine reasoning.
Direct involvement in shaping the technical architecture of AI/ML platform.
If you want to build the intelligence systems themselves, not just applications on top of them, we would like to hear from you.
Send your resume to hr@areta360.com and let’s build the future together.